THE EFFECT OF WORKING CAPITAL, RETURN ON ASSETS AND COMPANY SIZES ON THE CREDIT AMOUNT OF SMALL AND MEDIUM MICRO BUSINESSES IN NATIONAL BANKS IN INDONESIA PRE COVID-19

DOI:10.38035/DIJEFA Abstract: The research of "Effect of Working Capital, Return on Assets and Company Size on the Amount of Micro and Small Medium Enterprises Loans at National Banks in Indonesia in Pra COVID-19" was conducted using Multiple Linear Regression analysis tools using the help of SPSS 25 data processing applications. This research is the influence of Working Capital variable on the distribution of MSME loans with t arithmetic> t table (4.992> 2.048) with a significance value of 0.000 <0.05. The Return on Assets (ROA) variable does not affect the distribution of MSME loans to national banks in Indonesia in 2014-2018 with t count <t table (0.025 <2.048) with a significance value of 0.980> 0.05. The company size variable has a significant effect with the value of t count> t table (3.026> 2.048) with a significance value of 0.006 <0.05. Based on a simultaneous study of working capital, Return on Assets (ROA), and company size influence the distribution of MSME loans to national banks in Indonesia in 2014-2018 with a F table of 2.98 and a significance level of 0.05. Then F count> F table (12.041> 2.98) and sig. <0.05 (0,000 <0.05).


INTRODUCTION
COVID-19 has a huge influence on the world's economy, the Financial System Stability Committee (KSSK) predicts that the spread of Covid-19 will hit the Indonesian economy in 2020 to reach around 2.3% to -0.4%. Micro, small and medium enterprises (MSMEs) are predicted to be hardest hit by this condition, the COVID-19 outbreak is Previous research is used as a material for consideration and reference in this study, including ROA has no effect on lending is the result of research from Susan Pratiwi and Lela Hindasah (2014), as well as research from Suci Prihartini and I Made Dana [2018], that the influence CAR, NPL, and ROA on People's Business Credit Distribution have a significant effect. Based on the description above, the problem of this research can be formulated how the influence of working capital on MSME credit lending, Return On Assets to MSME credit lending, company size on MSME lending, working capital, Return on Assets, and the size of MSME lending companies national banks in Indonesia as credit suppliers for Pre COVID-19, so the hope of this study as a simple analysis in evaluating the distribution of Pre  Credit to banks in Indonesia.

LITERATURE REVIEW Definition of Working Capital
Working capital according to Brigham and Houston, translated by Ali (2014: 258), is all short-term assets, or cash-current assets, marketable securities, inventories and trade receivables. According to Kasmir (2012: 250), working capital is capital used to carry out company operations. The formula for working capital: Working Capital = Current assets -Current debt

Definition of Return On Assets (ROA)
According to Nogi S. Tangkilisan (2003: 251) Return on Assets is a measure of profitability that is better than the ratio of gross profit, operating ratio, acquisition of sales because it measures operating efficiency. This ratio shows the effectiveness of the company in using assets in accordance with its control to create revenue. The most common calculations on Return on Assets are: = The greater Return on Assets shows the company's performance is getting better. That is, Return on Asset is a reflection of the effective use of assets to increase corporate profits. Something that can increase profits, is certainly seen positively for stakeholders, including creditors.

Definition of Company Size (Size)
The size of the company can be seen from the total assets owned by the company (Sujoko andUgy, 2007: 45 in Wirawan, 2017). The size of the company is the number and type of production capacity and capabilities of the company or the number and type of services that can be provided by the company simultaneously to its customers (Niresh andVelnampy, 2014: 57 in Wirawan, 2017). Company size can be measured by the formula: Size = Ln of Total Assets.

Hypothesis
In this study, the hypothesis that the authors put forward is : Hypothesis-1 : There is a positive influence of working capital on the distribution of MSME loans to national banks in Indonesia in 2014-2018 Hypothesis-2 : There is a positive effect of Return on Assets on the distribution of MSME loans to national banks in Indonesia in 2014-2018 Hypothesis-3 : There is a positive influence of company size on the distribution of MSME loans at national banks in Indonesia in 2014-2018 Hypothesis-4 : Simultaneously there was a positive influence between working capital, Return on Assets, and company size on the distribution of MSME loans to national banks in Indonesia in 2014-2018

RESEARCH METHODS
This research was conducted using the associative method, which is a method used to determine the effect or also the relationship between two or more variables (Sugiyono, 2014: 13). The data used in this study is quantitative data by testing associative hypotheses.

Data Types and Sources
The type of data used in this study is quantitative data in the form of financial statements of national bank companies distributing MSME credit in Indonesia Pre COVID-19 [during 2014-2018. Data sources used are secondary data obtained through searches of internet media from the company's official website, other sources in the form of journals needed, as well as other sources that can be used in related research.

Sample and Population
This research uses Proportionate Stratified Random Sampling method, which is a technique used if the population has members that are not homogeneous and proportionally stratified. As for the population in this study are national banking companies that distribute Pre COVID-19 MSME loans [during 2014-2018 The criteria in taking the sample of this study were national banks that distributed Pre COVID-19 MSME loans [during 2014-2018].

Data collection technique
The data source used in this study is internal secondary data. Internal secondary data on the financial records of MSME credit suppliers in the form of corporate financial statements. The data source is taken from the published banking financial statements. Meanwhile, the data obtained will be processed to obtain working capital, ROA, and company size

Variable Operations
The variables used in this study consisted of independent variables and dependent variables. The independent variables in this study are working capital, Return on Assets (ROA), company size and the dependent variable in this study is the distribution of MSME loans to national banks in Indonesia Pre COVID-19 [during 2014-2018.

Data Analysis Techniques
This study uses Multiple Linear Regression analysis tools using statistical package software program SPSS (

FINDINGS AND DISCUSSION Descriptive statistics
After processing the data and testing the statistics using SPSS 25, the statistical results obtained from the data variables used in this study are as follows: Source: SPSS 25 output (data processed) Descriptive statistical test results in table 2 above show that the amount of data or n used in this study were 35 data samples taken from the financial statements of MSME credit channeling banks as many as 7 banks for the period 2014-2018. Working

Figure 2 Probability Plot Normality Test Results
Source: SPSS 25 output (data processed) The normality test aims to test whether in the regression model, the dependent variable, namely MSME Credit, and the independent variables namely working capital, ROA, and company size have normal data distribution or close to normal data. To test the normality of data, used the normal Probability Plot is detection by looking at the spread of data (points) on the diagonal axis on a graph.

Multicollinearity Test
This test is conducted to test whether the regression model found correlation between variables (independent). This test is done by looking at the Tolerance value and the value of Variance Inflation Factor (VIF). The cut off value commonly used to indicate multicollinearity is <0.10 or equal to VIF> 10 Table 3.

Multicollinearity test results with tolerance and VIF values
Source: SPSS 25 output (data processed) Based on Table 3 above, the working capital variable has a Tolerance value of 0.168, the ROA variable has a Tolerance value of 0.769, the company size variable has a Tolerance value of 0.151. Meanwhile, for the generated VIF value ie for the working capital variable has a VIF value of 5.955, the ROA variable has a VIF value of 1.301, and the firm size variable has a VIF value of 6.637. In accordance with the requirements of a variable it is said to have no correlation between variables or multicollinearity does not occur ie the value of the Tolerance value> 0.10 and the VIF value <10. Thus, it can be concluded that the variable under study does not have multicollinearity.

Heteroscedasticity Test
This test aims to test whether in the regression model Variance inequality occurs from the residuals of one observation to another. If the variance from one observation residual to another observation is fixed, it is called heteroscedasticity. A good regression model is one that does homoskesdasticity or does not occur heteroscedasticity. Multiple regression does not occur Heteroscedasticity if the data points spread above and below or not around the number 0, the data points do not collect just above or below it, the spread of data points may not form a wavy pattern widened then narrowed and widened widening data points should not be patterned. The results of the SPSS version 25 heteroscedasticity test can be seen as follows:

Autocorrelation Test
The autocorrelation test aims to test whether in the linear regression model there is a correlation of confounding errors in the t period with confounding errors in the t-1 period (Ghozali, 2013: 107). This test uses the Durbin-Watson test to determine whether there is autocorrelation in the regression model and the following Durbin-Watson values obtained through the processing of the regression model:  Source: SPSS 25 output (data processed) Based on Table 4 and Table 5 above, it shows that the DW value is 1.661. The value obtained will be compared with the table value using a significance value of 5%. The number of samples is 30 (n = 30) and the number of independent variables is 3 (k = 3), then in the Durbin-Watson table the lower bound value (dl) is 1.2138 and the upper limit value (du) is 1.6498. Furthermore, according to the autocorrelation rules, that a variable is declared to not occur autocorrelation if du <d <4-du. Then, 1,661 <1.6498 <4-1,6498. So it can be concluded that the research model does not occur autocorrelation.

Multiple Linear Regression Analysis
To measure the influence of working capital, ROA, and company size on MSME credit lending with multiple linear regression methods are as follows: Based on Table 6 above, the following equation can be formed: Y = a + b1X1 + b2X2 + b3X3 + ... MSME Credit = 3,050 + 0.315 working capital + 0.079ROA -0.161 company size The regression line equation is obtained, then the regression model can be interpreted as follows, a constant coefficient (Y) of 3.050: meaning that if there is no working capital, ROA, and company size, the betas value is 3.050. It means that if the amount working capital, ROA, and company size are equal to zero, then the UMKM Credit will be 3.050. Regression coefficient X1 (working capital) has a value of 0.315 and is positive, then if the value of working capital rises by 1 percent, MSME Credit will increase by 0.513 and vice versa, if working capital falls by 1 percent then MSME Credit will decrease by 0.513 assuming working capital is considered constant. Regression coefficient X2 (ROA) has a value of 0.079 and is positive, so if the ROA value increases by 1 percent, MSME Credit will increase by 0.079 and vice versa, if ROA falls by 1 percent then MSME Credit will decrease by 0.079 assuming ROA is considered constant . Coefficient X3 (company size) has a value of -0.161 and has a negative value, so if the value of company size increases by 1 percent, MSME Credit will decrease by -0,161 and vice versa, if company size decreases by 1 percent then MSME Credit will increase by 0.161 with assuming company size is considered constant.

T test (partial)
This test is carried out to prove whether working capital (X1), ROA (X2), and company size (X3) partially have an influence on MSME credit. The following are the results of the calculation of the t test and its significance level in this study: Source: SPSS 25 output (data processed) Based on Table 7 above, it is known that t calculate the working capital variable of 4.992, while for the ROA variable of 0.025, and the company size variable of -3.026. The hypothesis in this study is a two-way hypothesis for the t-table values obtained.
Based on the calculation above, with the amount of data (n) 35 and a significance value of 0.05, a t-table value of 2.048 is obtained. It turns out, for the working capital variable (X1) has a value of t-count> t-table (4.992> 2.048) with a significance value of 0.000 <0.05. Thus, Ho is not accepted and Ha is accepted, or in other words working capital has a significant effect on the distribution of MSME Credit.
The ROA variable (X2) in the above calculation has a t-count value <t-table (0.025 <2.048) with a significance value of 0.980> 0.05, this means that Ho is accepted and Ha is not accepted, or in other words ROA has no significant effect towards MSME credit distribution. The company size variable (X3) in the above test has a value of t-count> t-table (3.026> 2,048) with a significance value of 0.006 <0.05, thus Ho is not accepted and Ha is accepted, or company size has an effect on MSME credit distribution. This is because the value of t is absolute, that is the minus that is in the calculation results is not considered.

F Test
The F test basically shows whether all the independent variables referred to in the model have a shared influence on the dependent variable. In this test also uses a significance level of 5% or 0.05. Calculation of the results of this test can be seen from the following

Determination Coefficient Test
The coefficient of determination essentially measures how far the model's ability to explain the variation of the dependent variable. Source: SPSS 25 output (data processed) Based on Table 9 above, obtained values for R Square (R²) is 0.486. It means that 48.6% of the MSME Credit dependent variable is explained by the working capital, ROA, and company size independent variables, while the remaining 51.4% is explained by other factors beyond the regression model that cannot be included in this study.

DISCUSSION
Based on the results of tests that have been carried out through data processing above, then in this study there are a number of things that can be explained as follows:

The Effect of Working Capital on the Distribution of MSME Credit
Based on the results of tests that have been carried out using a partial hypothesis test shows that the working capital variable (X1) does not significantly influence the MSME credit at national banks in Indonesia in the 2014-2018 period. This can be seen based on the results of statistical tests, that the value of t arithmetic> t table (4.992> 2.048) with a significance value of 0.000 <0.05. This is caused by the forming components of working capital in which there are current assets, one of which is current assets given loans. This result is in line with research conducted by Sabiela Rahmani Subekti (2010) that there is a significant influence between working capital on lending.

The Effect of Return on Assets (ROA) on the Distribution of MSME Credit
Based on the test results stated above, that partially Return on Assets (ROA) has no significant effect on MSME loans. This is evident from the statistical test results obtained, where the value of t count <t table (0.025 <2.048) with a significance value of 0.980> 0.05. This indicates that the operating efficiency measured by ROA does not affect the amount of credit extended. This is because there are a number of prioritized funding besides credit funding (Susan Pratiwi & Lela Hindasah, 2014). This is consistent with research conducted by Susan Pratiwi and Lela Hindasah (2014) that Return on Assets (ROA) does not affect of MSME credit distribution.

The Effect of Company Size on the Distribution of MSME Credit
Based on the test results described above, that the size of the company significantly influences the distribution of MSME loans. This can be seen from the results of t test calculations, that the value of t arithmetic> t table (3.026> 2.048) with a significance value of 0.006 <0.05. This means, for each company size increase, it will increase the number of MSME loans that are distributed. The results of this study are in line with previous research conducted by Adnan, Ridwan and Fildzahc (2016) which states that a bank that has a large size means it has a large potential for wealth or funds, so as to increase the credit extended by banks.

CONCLUSION AND SUGGESTION Conclusion
Based on the results of the discussion, it can be concluded that, the working capital variable has a t value> t table (4.992> 2.048) with a significance value of 0.000 <0.05. Working capital variable significantly influences the distribution of MSME loans to national banks in Indonesia in 2014-2018. The Return on Assets (ROA) variable has a t value <t table (0.025 <2.048) with a significance value of 0.980> 0.05. The Return on Assets (ROA) variable does not affect the distribution of MSME loans to national banks in Indonesia in 2014-2018. The company size variable has a value of t count> t table (3.026> 2.048) with a significance value of 0.006 <0.05. The company size variable significantly influences the distribution of MSME loans to national banks in Indonesia in 2014-2018. Based on a simultaneous study of working capital, Return on Assets (ROA), and company size influence the distribution of MSME loans to national banks in Indonesia in 2014-2018 with a F table of 2.98 and a significance level of 0.05. Then F count> F table (12.041> 2.98) and sig. <0.05 (0,000 <0.05).